We report on the development of general-purpose algorithms
for global parameter minimization in scientific applications
where complex data sets resemble a pattern recognition
problem. Our basic approach is to implement model
calculations for the physical problem in parallel on a
Beowulf cluster, with a genetic algorithm to optimize the
parameters of the calculation and a neural network trained
on observational data to compute the fitness function for
members of the genetic algorithm population in each
successive generation. We shall use this approach to
investigate galaxy collisions using a gravity tree plus SPH
hydrodynamics implemented with the code GADGET[1], our own
genetic algorithm code for global parameter minimization,
and our own neural network code for comparison of
calculations with observational data. We shall also discuss
other potential applications such as to the analysis of
element production in large networks for r-process, hot-CNO,
and rp-process calculations.